Association-Aware GNN for Precoder Learning in Cell-Free Systems

Abstract

Deep learning has been widely recognized as a promising approach for optimizing multi-user multi-antenna precoders in traditional cellular systems. However, a critical distinction between cell-free and cellular systems lies in the flexibility of user equipment (UE)-access point (AP) associations. Consequently, the optimal precoder depends not only on channel state information but also on the dynamic UE-AP association status. In this paper, we propose an association-aware graph neural network (AAGNN) that explicitly incorporates association status into the precoding design. We leverage the permutation equivariance properties of the cell-free precoding policy to reduce the training complexity of AAGNN and employ an attention mechanism to enhance its generalization performance. Simulation results demonstrate that the proposed AAGNN outperforms baseline learning methods in both learning performance and generalization capabilities while maintaining low training and inference complexity.

Figures (6)

• Figure 1: Parameter-sharing structure of $\tilde{\mathbf{W}}$ ($\hat{\mathbf{W}}$ has the same structure).
• Figure 2: Illustration of the graph of AAGNN with $K = 3$, $N = 3$ and $M = 3$ (TX$_{m_n}$ denotes the $n$-th antenna vertex of AP$_m$).
• Figure 3: Parameter-sharing structures of $\tilde{\mathbf{O}}$, $\tilde{\mathbf{P}}$ and $\tilde{\mathbf{Q}}$ ($\hat{\mathbf{O}}$, $\hat{\mathbf{P}}$ and $\hat{\mathbf{Q}}$ have the same structures).
• Figure 4: Learning performance vs. the number of training samples with $N = 16$ and $M = 4$.
• Figure 5: Generalization performance: (a) generalization to the number of UEs with $N = 16$ and $M = 4$, and (b) generalization to the number of antennas with $K = 8$ and $M = 4$.